The first part, Concerns of Luminaries , is made of 11 of the most influential articles related to future AI progress, presented in chronological order. Reading this part felt like navigating through history, chatting with Bill Joy and Ray Kurzweil. The second part, Responses of Scholars , is comprised of 17 chapters written by academics specifically for this book.
The book starts with the influential essay written by Bill Joy at the beginning of the century. The author, co-founder of Sun Microsystems, author of vi and core contributor of BSD Unix, recounts a decisive talk he had with Ray Kurzweil in that changed his perspective on the future of the technology.
The author's knowledge about nanotechnology comes from Feynman's talk There is plenty of room at the bottom and Drexler's Engines of Creation Joy believed nanotechnology didn't work, until he discovered that nanoscale molecular electronics was becoming practical. This raised his awareness about Knowledge-Based Mass Destruction amplified by self-replication in nanotechnologies.
I really enjoyed this essay on GNR by someone who contributed this much to technological progress. It's one of the only chapter in The Singularity is Near that addresses existential risk. Responding to Bill Joy's article, Kurzweil presents several ways to build defensive technology to avoid existential risk: For Kurzweil, "Intelligence is inherently impossible to control". His proposition to align AI with human values is "to foster those values in our society today and [go] forward. I was pleasantly surprised to see Kurzweil addressing Yudkwosky's Friendly AI and Bostrom's framework of existential risk in He appears to know very well the risks associated with an intelligence explosion, but have different opinions on how to deal with them.
This is an essential AI paper describing the core "drives" that any sufficiently advanced intelligence would possess e. Max Tegmark proposes a more physicist-oriented approach to Friendly AI. The questions I found the most interesting are:.
Interesting to have Tegmark's physics-oriented perspective. This paper presents a method of producing a self-replicating AI with a safe goal of intelligence distillation , where the key metric is the description length of an AI capable of open-ended recursive improvement. This was the first technical chapter, and it was much more difficult to follow. It feels like Drexler author of the Engines of Creation on nanotechnology which was cited in the beginning by Bill Joy is answering the previous chapters that often cited his work!
In the conclusion, the author gives more meta-advices on AI Safety research, like bridging the AI research agenda gap or enriching the conceptual universe, which I found really interesting. The paper surveys methods and problems for the value learning problem.
One of the key component is an inductive value learning system that would learn to classify outcomes , using some labeled value-learning data. Soares then considers how to adapt the algorithm for different issues e. To solve those problems, such an algorithm would need to be able to learn from sparse data and to identify a referent of a label of the training data for any given model of reality. This paper was very enjoyable to read. Clear and straight to the point. It surveys multiple important problems and answers them with a simple and concrete method: Essentially, it's possible to send adversarial examples to a classifier that will be misclassified, even in a real world setting e.
Even assuming the difference between an adversarial example and a training example is much smaller in magnitude than a certain noise, the adversarial example can still be misclassified while the noise correctly classified. Straight to the point paper with a bunch of concrete experiments, pictures and results.
Made me consider the Security concerns in a more pragmatic way e. Multiple paths of AI development are discussed e.
Other concepts such as Sapience, the control problem or ethics are considered. The essay explains superficially multiple concepts. The author seems to be trying to give an intuition for a "Skynet-scenario" to a general audience. This chapter felt weak and fuzzy compared to the rest. This could lead to global information warfare where humans can't compete alone with computational propaganda.
This made me update my beliefs about the importance of computational propaganda this century. However, I found that the chapter focused too much on the short-term. For instance, in the definition of AGI in the glossary , it says: In the short- and medium-term, AI labs would still conduct original research to build skills, keep up with the state of the art and have a monopoly on their research for a few months while competitors are catching up. So openness would result in accelerating AI progress. In the long-term, the final stages for building AGI will likely be much more competitive.
To avoid a tight race where AI Safety is dismissed, a singleton scenario is preferable. Another possibility to take into account is hardware overhang. If algorithmic breakthrough is what leads to an intelligence explosion, then openness would favor small groups that don't have access to advanced hardware.
On the other hand, if hardware is the decisive factor, then openness would favor "elite", or already established labs. Openness in AI development would clearly help AI Safety research, for it's difficult to work on making AI systems safer if those systems are kept secret. AI Safety could benefit more from openness than AI Capability, because of a need of external perspectives. However, altruistic outsiders are already likely to openly contribute to AI Safety. I found this paper very dense. It made me deeply consider the strategic intricacies of openness. I think this chapter is fundamental for this book and I was happy to have it at the end of this part.
Two intelligences are differentiated: GI is essentially the intelligence of all human genomes or the intelligence of evolution. GI provides control for human reproduction and therefore controls HI. If all intelligences prove to be inherently dangerous, then we could use the same human principles that made humanity safe for AI 22 principles are considered. This chapter appeared to me both concrete and ambitious because of the precise description of those 22 proposed principles.
In this chapter, the mathematician Edward Frenkel tells his story. In particular, he describes the defense mechanism after a traumatism called dissociation, and how it affected his research life. His point is the following: His story touched me, but I felt it did not directly address AI Safety. Can the AI send a lethal message? The math to prove the bounds were easy to follow. The methods seem to only work if the humans know what method the AI is using and want to know the content of the message. There is a trade-off between spending time trying to solve motivation drift, representation drift, and reducing the risks of hacking.
Short and clear chapter. It was a bit redundant considering the chapter "The basic AI drives" and "The value learning problem". This chapter addresses verifiability using a framework for deontology. The main result is that verifying if an agent will always exhibit a good behaviour given a deontology is not computable. This result is then applied to AI Safety strategy. I found the math symbols unnecessary.
More generally, the chapter was too technical for a non-expert audience. After a general introduction on what adversarial examples are, the chapter presents a taxonomy of different attacks depending on the level of information about the model being attacked. One of the key claims is that AI security against adversarial examples should not be neglected, because given enough time and efforts an attacker can build a pseudo-model and cause harm.
This chapter was very well presented and clear. These graphs also admit of howlers and counterexamples as anyone knows. I am more interested in the idea of developing "stupid machines" that function more like neural networks and less like probability maximizers. The human brain is fundamentally in my view anyway a stupid-machine, full of crazy workarounds and faulty logic.
The correct solution or path is virtually never the one evolution comes up with, it just grinds it out with massive armies of neurons and interconnections and lots of trial and error, but nothing one would call a computation, as in Turing machines. Elegant algorithms for computer vision have, I believe, nothing to do with the way the brain constructs the visual image. Dec 19, Arjun rated it it was amazing Shelves: A fantastic textbook that's not only a great introduction to AI but also serves as a survey course in technical writing. Re-reading some earlier chapters taught me how much I missed on a first read or forgot.
The first 10 chapters or so are the A fantastic textbook that's not only a great introduction to AI but also serves as a survey course in technical writing. The first 10 chapters or so are the best and the second half of the book can be a bit of a trudge as it devolves into mathematical masturbation.
A lot of the chapters are better served by other resources — I highly recommend the CS lectures from UC Berkeley for supplementation. Unfortunately, some chapters are straight up bad the chapter on Philosophical Foundations comes to mind , but these tend to be few and far between. Despite that, there is no more comprehensive book on AI. Read this, re-read this, and treat it with care — you will reap the rewards for a long time to come. Jul 01, Luis rated it liked it Shelves: Lacks good solved exercises. Very short on detail in some areas such as Neural Networks.
However it does provide a good theoretical introduction to many subjects. I liked the chapters on search. Mar 07, Nakosy rated it really liked it. It was written more like a text book for undergrads with extensive coverage of many topics.
However, I was looking for more in-depth information on knowledge representation. But, it was too superficial for my need. May be, in 3rd edition it encompassed the latest ideas in this area. Dec 17, Drew rated it really liked it Shelves: A comprehensive course in modern AI topics. While the book is dense with information, the authors provide clear explanations that will be easily picked up by the careful reader.
An excellent companion to an undergraduate course in artificial intelligence. Jan 05, James Ravenscroft rated it it was amazing Shelves: This is THE book to read on anything to do with modern artificial intelligence. I regard this as my personal bible and would recommend it to anyone who is involved in technical artificial intelligence. Um excelente livro para quem quer estudar fundamentos de IA, recomendo.
Mar 01, Jaslyn rated it it was amazing.
Mar 23, Shahriar Hossain is currently reading it. We call ourself Homo sepiens - man the wise This is the most complete and comprehensive book I read on a subject of Artificial Intelligence so far and it's very well written as well. If you plan diving into AI really seriously and you are keen to invest some good amount of time going through pages of this book then I really recommend it for you. Great addition to this book is A.
Last three months I spent every day with both this book and A. It's a pricey book. It was used in my university on AI. It covers many AI topics including intelligent agents, searching, knowledge representation, machine learning, etc. There are enough examples, but not enough good and clear examples. The book is heavily biased towards First Order Logic as the way to do knowledge representation, making it good on Bayesian networks. Other topics like neural networks and machine vision would be better off read elsewhere. Overall, the book is not for light readi It's a pricey book.
Overall, the book is not for light reading - you need to really concentrate on what you're reading to understand it. Fantastic and comprehensive book on the different aspects of artificial intelligence AI. I am attending Stamford cs online class fall and I am also a member of the team translating the videos from English to French. Peter Norvig is a great teacher. The book and the videos complement each another very well. Even if you are not taking the course, or after you have took it, this book is a superb reference on the subject for graduate students and professionals alike.
I highly recommend i Fantastic and comprehensive book on the different aspects of artificial intelligence AI. I highly recommend it to anyone interested in the field. Jan 01, Patrick Jennings rated it liked it Shelves: Pretty much THE book to have on comprehensive artificial intelligence. A Modern Approach is used in schools and universities across the globe. Don't expect implementations in anything but general pseudocode in this book.
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Although, you may check the repository at aima. One of the best technical books I have read, albeit on a hard topic. It's quite readable and a lot better than my AI lectures ever were. Must admit to only reading the first 12 chapters though. Its main weakness lies in the lacking coverage of "new AI" topics, such as evolutionary algorithms.
What a nice and useful book! All the main AI before or so is here and the accompanying code in lisp and python is a good way to learn about the implementation details. I will surely remember this book when I am writing a similar one! Mar 20, Jamison Dance rated it really liked it. The classic introduction to the field. Peter Norvig and Stuart Russell are great writers, and do a good job of explaining the concepts. Sometimes their bias shows, but that is something I am willing to put up with for a book that covers the field so well.
Oct 12, Tasnim Dewan Orin rated it it was amazing Shelves: This is holy grail i. I love the examples and everything it taught me while learning basic of AI. May 27, Mahmoud Adly rated it liked it. That paradox of loving AI and hating the journey the center of math.
Any ways, it is a reference after all. At least I know where to go when I have a problem. Mar 20, ali is currently reading it Recommends it for: Extremely dense book that covers just about every subgroup of AI. Focused a little too much on the 'logical' sections that are then blown away a few hundred pages later when you learn that the real world is too complex to make logical rule systems for any worthwhile problem.
That's where machine learning comes in. Yet, the chapters on ML while still amazing are not as in depth as the logic chapters that take up the middle few hundred pages. The second half of the book is also extremely math he Extremely dense book that covers just about every subgroup of AI. The second half of the book is also extremely math heavy and I will most likely have to revisit some chapters when I get a better understanding of the advanced math needed for AI and ML.
Mar 23, Ayush Bhat rated it it was ok. The book wastes much space with explanations of the trivial and then leaves a big gap in helping the student to understand how to build algorithms to solve the classic problems.